In the oil and gas industry, modeling of the subsurface is typically utilized for visualization and to assist with analyzing the subsurface volume for potential locations for hydrocarbon resources. Accordingly, various methods exist for estimating the geophysical properties of the subsurface volume (e.g., information in the model domain) by analyzing the recorded measurements from receivers (e.g., information in the data domain) provided that these measured data travel from a source, then penetrate the subsurface volume represented by a subsurface model in model domain, and eventually arrive at the receivers. The measured data carries some information of the geophysical properties that may be utilized to generate the subsurface model.
Traditional interpretation systems in the geosciences are product oriented instead of process oriented. The interpreter typically loads some data for a hard disk, performs an interpretation process on this data, and stores the result on disk. What are stored are the outputs or products that may become inputs for later interpretation step or processes. After lengthy manual interpretation steps, the detailed relations between different products are not captured. At times, even the provenance of a product may not be captured, which may be a result of inconsistencies between products. Accordingly, to prevent the loss of valuable information, the data is accumulated instead of being deleted. Further, one or more interpreters may repeat the same interpretation step because an underlying input changed or details were not captured. That is, conventional interpretation processes are not efficient.
As an example, an interpreter may select a horizon in a manual, assisted or automated manner for a typical seismic interpretation system. Depending on horizon prominence and data quality, the interpreter may spend a few minutes to one or more weeks on one horizon. Then, the interpreter picks a second and a third horizon, which may involve similar time periods for analysis. While picking the third horizon, the interpreter may encounter an issue where the third horizon intersects the second one. Clearly, this intersection violates expectations or the law of stratigraphic superposition. At this point, the interpreter has to determine whether the problem is with the second horizon or third horizon and attempts to correct the situation by editing and reinterpreting the horizons in a manual or computer-assisted manner. However, the editing may result in intersections with other horizons. In conventional interpretation systems, numerous interpretation objects, such as surfaces or faults, are generated and stored independently from each other. These inconsistencies between the interpretation objects may remain undetected and cascade through multiple interpretation products until identified, which may require determining the root cause of the inconsistencies and/or recreating the interpretation products.
These problems are further evident in seismic interpretation systems that involve pattern recognition techniques. Typically, interpreters learn about certain patterns, which are used along with experience to identify patterns in the measurement data. Yet, the nature of many patterns do not become concrete or formal enough to be identified and recorded and, as a result, experience is not captured. This also hinders the building of an automated pattern recognition system, as there is no natural language in which to exchange information on appropriate levels of abstraction. There is a gap between the descriptions provided by experienced interpreters and the descriptions required to instruct a computer.
As the recovery of natural resources, such as hydrocarbons rely, in part, on a subsurface model, a need exists to enhance subsurface models of one or more geophysical properties. In particular, a need exists to enhance the seismic interpretation systems to be able to instruct a computer how to find patterns in data and allows to describe those patterns in a manner that is abstract enough to hide many of the technical detail.